Abstract:

Fluorescence video angiography is used in neurosurgery to intraoperatively monitor the vascular func-tion, namely the blood flow. This is done by injecting the dye Indocyanine green (ICG) intravenously. After excitation by a near-infrared light source, the fluorescence signal is captured by a camera system. The recorded signal is used to qualitatively assess the vascular function during the intervention. This provides the surgeon with an immediate feedback of the quality of his surgery. Nevertheless, this qualitative assessment needs to be extended and a quantitative value should be calculated to assist the surgical staff. This step requires a standardized and validated test setup mimicking cerebral vessels for studies, such as measurement of the flow and flow profile. This includes the confirmation of the suita-bility of the investigation site in the phantom. Therefore, a flow phantom is designed according to the requirements and set up. The requirements include a variable diameter of the vessel mimicking tubes, variable flow range within the clinical relevant range, a handy and precise injection system with an ini-tial ICG concentration which minimizes quenching effects, a non-toxic and low cost blood analogue with similar viscosity as human blood and finally a last requirement which need more explanation. Re-al blood should not be used due to the contamination of the pump, so water is used as flow media. But the ICG is dissolved in a protein solution and should be surrounded by a protein solution to ensure mixing and diffusion into the same solution media, so the ICG should not get into touch with the flow media water. The investigation sites are given in the ranges which are confirmed to be suitable. The flow phantom provides a consistent testing environment and will be used to conduct studies analyzing the suitability of different methods to assess the flow by fluorescence imaging.

Abstract:

Today the vascular function after interventions as Bypass surgeries are checked qualitatively by observing the blood dynamics inside the vessel via Indocyanine Green (ICG) Fluorescence Angiography. This state-of-the-art should be upgraded and has to be improved and converted towards a quantitatively measured blood flow. Previous approaches show that the blood flow measured from fluorescence angiography cannot be easily calibrated to a gold standard reference. In order to systematically address the possible source of error we investigate as a first step the discretization error in a camera-based measurement of the vessel’s geometry. In order to generate an error-free ground truth, a vessel model has been developed based on mathematical functions. This database is then used to determine the error in discretizing the centerline of the structure and estimate its effects on the accuracy of the flow calculation. As result the model is implemented according to the conditions which are set up to ensure transferability on camera-based segmentations of vessels. In this paper the relative discretization error for estimating the centerline length of segmented vessels could be calculated in the range of 6.3%. This would reveal significant error propagated to the estimation of the blood flow value derived by camera-based angiography.

Abstract:

Optical image processing is part of many applications used for brain surgeries. Microscope camera, or patient movement, like brain-movement through the pulse or a change in the liquor, can cause the image processing to fail. One option to compensate movement is feature detection and spatial allocation. This allocation is based on image features. The frame wise matched features are used to calculate the transformation matrix. The goal of this project was to evaluate different feature detectors based on spatial density and temporal robustness to reveal the most appropriate feature. The feature detectors included corner-, and blob-detectors and were applied on nine videos. These videos were taken during brain surgery with surgical microscopes and include the RGB channels. The evaluation showed that each detector detected up to 10 features for nine frames. The feature detector KAZE resulted in being the best feature detector in both density and robustness.